Is my network module preserved and reproducible?
Is my network module preserved and reproducible?
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DOI：
10.1371/journal.pcbi.1001057
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发表时间：
2011-01-20
影响因子：
4.3
通讯作者：
Horvath S
中科院分区：
文献类型：
--
作者：
Langfelder P;Luo R;Oldham MC;Horvath S
在许多应用中，人们感兴趣的是确定网络模块的哪些属性随条件而变化。例如，为了验证一个模块的存在，需要证明它在一个独立的测试网络中是可重现的（或保留的）。在这里，我们研究了几种类型的网络保存统计，不需要在测试网络中的模块分配。我们通过底层网络的类型来区分网络保存统计。一些保留统计量是为一般网络（由邻接矩阵定义）定义的，而另一些则仅为相关网络（基于数值变量之间的成对相关性构建）定义。我们的应用程序表明，相关性结构有利于定义特别强大的模块保存统计。我们说明，评估模块保存一般不同于评估集群保存。我们发现，这是有利的，聚合多个保存统计到摘要保存统计。我们举例说明了这些方法在六个基因共表达网络应用中的使用，包括1）小鼠组织中胆固醇生物合成途径的保存，2）人类和黑猩猩脑网络的比较，3）人类和黑猩猩脑网络之间选定的KEGG途径的保存，4）人类皮层网络的性别差异，5）小鼠肝脏网络的性别差异。虽然我们没有发现人类大脑皮层网络中存在性别特异性模块的证据，但我们发现几个人类大脑皮层模块在黑猩猩中保存得较少。特别是，凋亡基因在人类和黑猩猩之间差异共表达。我们的模拟研究和应用表明，模块保存统计是有用的研究网络的模块结构之间的差异。数据、R软件和附带的教程可以从以下网页下载：http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation。在网络应用中，人们经常对研究模块是否在多个网络中被保留感兴趣。例如，为了确定基因通路在某种条件下是否受到干扰，可以研究其连接模式是否不再保留。未保存的模块可以是生物学上不感兴趣的（例如，反映数据异常值）或感兴趣的（例如，反映性别特定模块）。研究模块保持的一个直观方法是交叉制表模块成员。但这种方法通常无法解决节点之间连接模式的保留问题。因此，基于交叉制表的方法通常不能认识到网络模块的重要方面被保留。交叉制表方法使得很难证明一个模块没有被保留。弱语句（“参考模块不与任何识别的测试集模块重叠”）在实践中不如强语句（“无论模块检测过程的参数设置如何，都无法在测试网络中找到模块”）相关。模块保存统计有重要的应用，例如，我们表明，在人类皮层网络中的凋亡基因的布线不同于黑猩猩。
In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation. In network applications, one is often interested in studying whether modules are preserved across multiple networks. For example, to determine whether a pathway of genes is perturbed in a certain condition, one can study whether its connectivity pattern is no longer preserved. Non-preserved modules can either be biologically uninteresting (e.g., reflecting data outliers) or interesting (e.g., reflecting sex specific modules). An intuitive approach for studying module preservation is to cross-tabulate module membership. But this approach often cannot address questions about the preservation of connectivity patterns between nodes. Thus, cross-tabulation based approaches often fail to recognize that important aspects of a network module are preserved. Cross-tabulation methods make it difficult to argue that a module is not preserved. The weak statement (“the reference module does not overlap with any of the identified test set modules”) is less relevant in practice than the strong statement (“the module cannot be found in the test network irrespective of the parameter settings of the module detection procedure”). Module preservation statistics have important applications, e.g. we show that the wiring of apoptosis genes in a human cortical network differs from that in chimpanzees.
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影响因子：
4.3
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影响因子：
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作者：
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通讯作者：
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